What are some use cases of few-shot learning?
Few-short learning (FSL) can be useful for many (if not all) machine learning problems, including supervised learning (regression and classification) and reinforcement learning.

The paper Generalizing from a Few Examples: A Survey on Few-Shot Learning (2020) provides an overview (including examples of applications and use cases) of FSL. Their definition of FSL provided is based on Tom Mitchell's famous definition of machine learning.


  Definition 2.1 (Machine Learning [92, 94]). A computer program is said to learn from experience $E$ with respect to some classes of task $T$ and performance measure $P$ if its performance can improve with $E$ on $T$ measured by $P$.


Here's the definition of FSL.


  Definition 2.2. Few-Shot Learning (FSL) is a type of machine learning problems, specified by $E$, $T$ and $P$, where $E$ contains only a limited number of examples with supervised information for the task $T$.


Specific examples of applications of FSL are


character generation
drug toxicity discovery
sentiment classification from short text
object recognition